Overview

Dataset statistics

Number of variables28
Number of observations87067
Missing cells468228
Missing cells (%)19.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory83.2 MiB
Average record size in memory1001.6 B

Variable types

CAT15
NUM13

Warnings

Date Rptd has a high cardinality: 167 distinct values High cardinality
DATE OCC has a high cardinality: 167 distinct values High cardinality
Crm Cd Desc has a high cardinality: 126 distinct values High cardinality
Mocodes has a high cardinality: 38482 distinct values High cardinality
Premis Desc has a high cardinality: 280 distinct values High cardinality
Weapon Desc has a high cardinality: 72 distinct values High cardinality
LOCATION has a high cardinality: 28410 distinct values High cardinality
Cross Street has a high cardinality: 3533 distinct values High cardinality
Rpt Dist No is highly correlated with AREAHigh correlation
AREA is highly correlated with Rpt Dist NoHigh correlation
Crm Cd 1 is highly correlated with Crm CdHigh correlation
Crm Cd is highly correlated with Crm Cd 1High correlation
LON is highly correlated with LATHigh correlation
LAT is highly correlated with LONHigh correlation
Status Desc is highly correlated with StatusHigh correlation
Status is highly correlated with Status DescHigh correlation
Mocodes has 11129 (12.8%) missing values Missing
Vict Sex has 10602 (12.2%) missing values Missing
Vict Descent has 10604 (12.2%) missing values Missing
Weapon Used Cd has 55243 (63.4%) missing values Missing
Weapon Desc has 55243 (63.4%) missing values Missing
Crm Cd 2 has 79995 (91.9%) missing values Missing
Crm Cd 3 has 86837 (99.7%) missing values Missing
Crm Cd 4 has 87059 (> 99.9%) missing values Missing
Cross Street has 71476 (82.1%) missing values Missing
DR_NO is highly skewed (γ1 = -112.51093) Skewed
DR_NO has unique values Unique
Vict Age has 20917 (24.0%) zeros Zeros

Reproduction

Analysis started2020-12-12 23:00:41.417909
Analysis finished2020-12-12 23:01:07.605945
Duration26.19 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

DR_NO
Real number (ℝ≥0)

SKEWED
UNIQUE

Distinct87067
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201087898.8
Minimum10304468
Maximum209915309
Zeros0
Zeros (%)0.0%
Memory size680.3 KiB
2020-12-12T18:01:07.696023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum10304468
5-th percentile200111865.3
Q1200604736
median201108343
Q3201606708.5
95-th percentile202009995.7
Maximum209915309
Range199610841
Interquartile range (IQR)1001972.5

Descriptive statistics

Standard deviation891794.4872
Coefficient of variation (CV)0.0044348491
Kurtosis24058.17504
Mean201087898.8
Median Absolute Deviation (MAD)500880
Skewness-112.51093
Sum1.750812009e+13
Variance7.952974075e+11
MonotocityNot monotonic
2020-12-12T18:01:07.783098image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2008063981< 0.1%
 
2014117531< 0.1%
 
2001066171< 0.1%
 
2005059781< 0.1%
 
2001107151< 0.1%
 
2005052661< 0.1%
 
2011060491< 0.1%
 
2017073521< 0.1%
 
2007066921< 0.1%
 
2003114291< 0.1%
 
2007087431< 0.1%
 
2012105141< 0.1%
 
2019109201< 0.1%
 
2002049371< 0.1%
 
2002110821< 0.1%
 
2002090351< 0.1%
 
2015074691< 0.1%
 
2019068301< 0.1%
 
2019047831< 0.1%
 
2012147131< 0.1%
 
2021084641< 0.1%
 
2010056861< 0.1%
 
2006063251< 0.1%
 
2014070901< 0.1%
 
2012084031< 0.1%
 
Other values (87042)87042> 99.9%
 
ValueCountFrequency (%) 
103044681< 0.1%
 
1901010861< 0.1%
 
1901010871< 0.1%
 
1903264751< 0.1%
 
1915015051< 0.1%
 
1919212691< 0.1%
 
2001000011< 0.1%
 
2001005011< 0.1%
 
2001005021< 0.1%
 
2001005041< 0.1%
 
ValueCountFrequency (%) 
2099153091< 0.1%
 
2099137061< 0.1%
 
2099106211< 0.1%
 
2021105171< 0.1%
 
2021105161< 0.1%
 
2021105131< 0.1%
 
2021105121< 0.1%
 
2021105111< 0.1%
 
2021105101< 0.1%
 
2021105081< 0.1%
 

Date Rptd
Categorical

HIGH CARDINALITY

Distinct167
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size680.3 KiB
03/02/2020 12:00:00 AM
 
674
06/10/2020 12:00:00 AM
 
642
02/03/2020 12:00:00 AM
 
639
02/24/2020 12:00:00 AM
 
632
02/18/2020 12:00:00 AM
 
629
Other values (162)
83851 
ValueCountFrequency (%) 
03/02/2020 12:00:00 AM6740.8%
 
06/10/2020 12:00:00 AM6420.7%
 
02/03/2020 12:00:00 AM6390.7%
 
02/24/2020 12:00:00 AM6320.7%
 
02/18/2020 12:00:00 AM6290.7%
 
01/29/2020 12:00:00 AM6270.7%
 
02/28/2020 12:00:00 AM6250.7%
 
03/09/2020 12:00:00 AM6130.7%
 
02/27/2020 12:00:00 AM6110.7%
 
03/11/2020 12:00:00 AM6090.7%
 
01/28/2020 12:00:00 AM6070.7%
 
01/23/2020 12:00:00 AM6060.7%
 
04/28/2020 12:00:00 AM6040.7%
 
04/27/2020 12:00:00 AM6040.7%
 
05/18/2020 12:00:00 AM6030.7%
 
02/25/2020 12:00:00 AM6030.7%
 
02/10/2020 12:00:00 AM6020.7%
 
01/31/2020 12:00:00 AM6020.7%
 
02/13/2020 12:00:00 AM6010.7%
 
02/07/2020 12:00:00 AM5970.7%
 
01/27/2020 12:00:00 AM5950.7%
 
04/20/2020 12:00:00 AM5940.7%
 
02/19/2020 12:00:00 AM5910.7%
 
01/22/2020 12:00:00 AM5910.7%
 
01/30/2020 12:00:00 AM5900.7%
 
Other values (142)7177682.4%
 
2020-12-12T18:01:07.884185image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:01:07.958749image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length22
Mean length22
Min length22

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
064462433.7%
 
231309416.3%
 
/1741349.1%
 
1741349.1%
 
:1741349.1%
 
11428717.5%
 
A870674.5%
 
M870674.5%
 
3281151.5%
 
5248741.3%
 
4239651.3%
 
6162280.8%
 
886000.4%
 
785600.4%
 
980070.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number121893863.6%
 
Other Punctuation34826818.2%
 
Space Separator1741349.1%
 
Uppercase Letter1741349.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
064462452.9%
 
231309425.7%
 
114287111.7%
 
3281152.3%
 
5248742.0%
 
4239652.0%
 
6162281.3%
 
886000.7%
 
785600.7%
 
980070.7%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/17413450.0%
 
:17413450.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
174134100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A8706750.0%
 
M8706750.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common174134090.9%
 
Latin1741349.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
064462437.0%
 
231309418.0%
 
/17413410.0%
 
17413410.0%
 
:17413410.0%
 
11428718.2%
 
3281151.6%
 
5248741.4%
 
4239651.4%
 
6162280.9%
 
886000.5%
 
785600.5%
 
980070.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A8706750.0%
 
M8706750.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1915474100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
064462433.7%
 
231309416.3%
 
/1741349.1%
 
1741349.1%
 
:1741349.1%
 
11428717.5%
 
A870674.5%
 
M870674.5%
 
3281151.5%
 
5248741.3%
 
4239651.3%
 
6162280.8%
 
886000.4%
 
785600.4%
 
980070.4%
 

DATE OCC
Categorical

HIGH CARDINALITY

Distinct167
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size680.3 KiB
05/30/2020 12:00:00 AM
 
750
01/01/2020 12:00:00 AM
 
729
01/31/2020 12:00:00 AM
 
649
02/02/2020 12:00:00 AM
 
648
02/28/2020 12:00:00 AM
 
640
Other values (162)
83651 
ValueCountFrequency (%) 
05/30/2020 12:00:00 AM7500.9%
 
01/01/2020 12:00:00 AM7290.8%
 
01/31/2020 12:00:00 AM6490.7%
 
02/02/2020 12:00:00 AM6480.7%
 
02/28/2020 12:00:00 AM6400.7%
 
05/29/2020 12:00:00 AM6360.7%
 
02/01/2020 12:00:00 AM6330.7%
 
02/07/2020 12:00:00 AM6300.7%
 
02/14/2020 12:00:00 AM6300.7%
 
02/21/2020 12:00:00 AM6260.7%
 
01/17/2020 12:00:00 AM6230.7%
 
02/13/2020 12:00:00 AM6150.7%
 
02/05/2020 12:00:00 AM6100.7%
 
01/24/2020 12:00:00 AM6100.7%
 
01/10/2020 12:00:00 AM6060.7%
 
02/10/2020 12:00:00 AM6040.7%
 
03/01/2020 12:00:00 AM5990.7%
 
02/08/2020 12:00:00 AM5960.7%
 
01/28/2020 12:00:00 AM5960.7%
 
02/26/2020 12:00:00 AM5950.7%
 
03/02/2020 12:00:00 AM5930.7%
 
02/22/2020 12:00:00 AM5930.7%
 
01/26/2020 12:00:00 AM5890.7%
 
01/15/2020 12:00:00 AM5880.7%
 
02/25/2020 12:00:00 AM5840.7%
 
Other values (142)7149582.1%
 
2020-12-12T18:01:08.039819image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:01:08.117886image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length22
Mean length22
Min length22

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
064556233.7%
 
231324816.4%
 
/1741349.1%
 
1741349.1%
 
:1741349.1%
 
11441797.5%
 
A870674.5%
 
M870674.5%
 
3280021.5%
 
5246321.3%
 
4236491.2%
 
6148090.8%
 
784420.4%
 
883280.4%
 
980870.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number121893863.6%
 
Other Punctuation34826818.2%
 
Space Separator1741349.1%
 
Uppercase Letter1741349.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
064556253.0%
 
231324825.7%
 
114417911.8%
 
3280022.3%
 
5246322.0%
 
4236491.9%
 
6148091.2%
 
784420.7%
 
883280.7%
 
980870.7%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/17413450.0%
 
:17413450.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
174134100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A8706750.0%
 
M8706750.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common174134090.9%
 
Latin1741349.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
064556237.1%
 
231324818.0%
 
/17413410.0%
 
17413410.0%
 
:17413410.0%
 
11441798.3%
 
3280021.6%
 
5246321.4%
 
4236491.4%
 
6148090.9%
 
784420.5%
 
883280.5%
 
980870.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A8706750.0%
 
M8706750.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1915474100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
064556233.7%
 
231324816.4%
 
/1741349.1%
 
1741349.1%
 
:1741349.1%
 
11441797.5%
 
A870674.5%
 
M870674.5%
 
3280021.5%
 
5246321.3%
 
4236491.2%
 
6148090.8%
 
784420.4%
 
883280.4%
 
980870.4%
 

TIME OCC
Real number (ℝ≥0)

Distinct1394
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1359.450837
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Memory size680.3 KiB
2020-12-12T18:01:08.188947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile110
Q1930
median1445
Q31900
95-th percentile2230
Maximum2359
Range2358
Interquartile range (IQR)970

Descriptive statistics

Standard deviation644.504302
Coefficient of variation (CV)0.4740916586
Kurtosis-0.7123729012
Mean1359.450837
Median Absolute Deviation (MAD)455
Skewness-0.4895123832
Sum118363306
Variance415385.7953
MonotocityNot monotonic
2020-12-12T18:01:08.269516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
120031233.6%
 
180025843.0%
 
170025212.9%
 
200024262.8%
 
190021832.5%
 
160020702.4%
 
150020632.4%
 
220020232.3%
 
210020072.3%
 
140018142.1%
 
130015721.8%
 
100015381.8%
 
230014991.7%
 
80014161.6%
 
110013181.5%
 
90013111.5%
 
112611.4%
 
173010831.2%
 
183010441.2%
 
15309741.1%
 
1009721.1%
 
19309511.1%
 
16309451.1%
 
20309441.1%
 
21308931.0%
 
Other values (1369)4653253.4%
 
ValueCountFrequency (%) 
112611.4%
 
26< 0.1%
 
310< 0.1%
 
413< 0.1%
 
53170.4%
 
67< 0.1%
 
75< 0.1%
 
86< 0.1%
 
94< 0.1%
 
101720.2%
 
ValueCountFrequency (%) 
2359490.1%
 
23585< 0.1%
 
23575< 0.1%
 
23562< 0.1%
 
23551180.1%
 
23545< 0.1%
 
23536< 0.1%
 
23522< 0.1%
 
23512< 0.1%
 
23501770.2%
 

AREA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.82582379
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Memory size680.3 KiB
2020-12-12T18:01:08.342579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median11
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.071161267
Coefficient of variation (CV)0.5608036288
Kurtosis-1.18751567
Mean10.82582379
Median Absolute Deviation (MAD)5
Skewness-0.002176267256
Sum942572
Variance36.85899912
MonotocityNot monotonic
2020-12-12T18:01:08.409137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
1257386.6%
 
152596.0%
 
349335.7%
 
1847895.5%
 
1445735.3%
 
1343915.0%
 
1543405.0%
 
643305.0%
 
2041644.8%
 
740744.7%
 
239244.5%
 
1138904.5%
 
838894.5%
 
938674.4%
 
1938074.4%
 
537584.3%
 
2136734.2%
 
1035984.1%
 
434864.0%
 
1733963.9%
 
1631883.7%
 
ValueCountFrequency (%) 
152596.0%
 
239244.5%
 
349335.7%
 
434864.0%
 
537584.3%
 
643305.0%
 
740744.7%
 
838894.5%
 
938674.4%
 
1035984.1%
 
ValueCountFrequency (%) 
2136734.2%
 
2041644.8%
 
1938074.4%
 
1847895.5%
 
1733963.9%
 
1631883.7%
 
1543405.0%
 
1445735.3%
 
1343915.0%
 
1257386.6%
 

AREA NAME
Categorical

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size680.3 KiB
77th Street
 
5738
Central
 
5259
Southwest
 
4933
Southeast
 
4789
Pacific
 
4573
Other values (16)
61775 
ValueCountFrequency (%) 
77th Street57386.6%
 
Central52596.0%
 
Southwest49335.7%
 
Southeast47895.5%
 
Pacific45735.3%
 
Newton43915.0%
 
N Hollywood43405.0%
 
Hollywood43305.0%
 
Olympic41644.8%
 
Wilshire40744.7%
 
Rampart39244.5%
 
Northeast38904.5%
 
West LA38894.5%
 
Van Nuys38674.4%
 
Mission38074.4%
 
Harbor37584.3%
 
Topanga36734.2%
 
West Valley35984.1%
 
Hollenbeck34864.0%
 
Devonshire33963.9%
 
Foothill31883.7%
 
2020-12-12T18:01:08.493709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:01:08.564771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length8
Mean length8.311794365
Min length6

Overview of Unicode Properties

Unique unicode characters39
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t686879.5%
 
o685099.5%
 
e636618.8%
 
l513817.1%
 
a449286.2%
 
s400505.5%
 
i356564.9%
 
r337974.7%
 
h300084.1%
 
n278793.9%
 
214323.0%
 
y202992.8%
 
w179942.5%
 
c167962.3%
 
N164882.3%
 
H159142.2%
 
S154602.1%
 
u135891.9%
 
p117611.6%
 
W115611.6%
 
7114761.6%
 
d86701.2%
 
m80881.1%
 
V74651.0%
 
b72441.0%
 
Other values (14)548907.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter58412580.7%
 
Uppercase Letter10665014.7%
 
Space Separator214323.0%
 
Decimal Number114761.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N1648815.5%
 
H1591414.9%
 
S1546014.5%
 
W1156110.8%
 
V74657.0%
 
C52594.9%
 
P45734.3%
 
O41643.9%
 
R39243.7%
 
L38893.6%
 
A38893.6%
 
M38073.6%
 
T36733.4%
 
D33963.2%
 
F31883.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t6868711.8%
 
o6850911.7%
 
e6366110.9%
 
l513818.8%
 
a449287.7%
 
s400506.9%
 
i356566.1%
 
r337975.8%
 
h300085.1%
 
n278794.8%
 
y202993.5%
 
w179943.1%
 
c167962.9%
 
u135892.3%
 
p117612.0%
 
d86701.5%
 
m80881.4%
 
b72441.2%
 
f45730.8%
 
g36730.6%
 
k34860.6%
 
v33960.6%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
21432100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
711476100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin69077595.5%
 
Common329084.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t686879.9%
 
o685099.9%
 
e636619.2%
 
l513817.4%
 
a449286.5%
 
s400505.8%
 
i356565.2%
 
r337974.9%
 
h300084.3%
 
n278794.0%
 
y202992.9%
 
w179942.6%
 
c167962.4%
 
N164882.4%
 
H159142.3%
 
S154602.2%
 
u135892.0%
 
p117611.7%
 
W115611.7%
 
d86701.3%
 
m80881.2%
 
V74651.1%
 
b72441.0%
 
C52590.8%
 
P45730.7%
 
Other values (12)450586.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
2143265.1%
 
71147634.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII723683100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t686879.5%
 
o685099.5%
 
e636618.8%
 
l513817.1%
 
a449286.2%
 
s400505.5%
 
i356564.9%
 
r337974.7%
 
h300084.1%
 
n278793.9%
 
214323.0%
 
y202992.8%
 
w179942.5%
 
c167962.3%
 
N164882.3%
 
H159142.2%
 
S154602.1%
 
u135891.9%
 
p117611.6%
 
W115611.6%
 
7114761.6%
 
d86701.2%
 
m80881.1%
 
V74651.0%
 
b72441.0%
 
Other values (14)548907.6%
 

Rpt Dist No
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1144
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1128.515534
Minimum101
Maximum2198
Zeros0
Zeros (%)0.0%
Memory size680.3 KiB
2020-12-12T18:01:08.641337image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile176
Q1628
median1149
Q31642
95-th percentile2071
Maximum2198
Range2097
Interquartile range (IQR)1014

Descriptive statistics

Standard deviation607.2339668
Coefficient of variation (CV)0.5380820631
Kurtosis-1.188787221
Mean1128.515534
Median Absolute Deviation (MAD)512
Skewness0.005967759125
Sum98256462
Variance368733.0904
MonotocityNot monotonic
2020-12-12T18:01:08.727911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1624340.5%
 
1113570.4%
 
6463510.4%
 
6453490.4%
 
14943410.4%
 
18223110.4%
 
1823040.3%
 
18022920.3%
 
1522860.3%
 
6662850.3%
 
18012840.3%
 
6362830.3%
 
3632770.3%
 
18422750.3%
 
7652650.3%
 
2452570.3%
 
12682560.3%
 
12492510.3%
 
6472480.3%
 
12392460.3%
 
1532430.3%
 
12562420.3%
 
21562410.3%
 
1192360.3%
 
12692350.3%
 
Other values (1119)7991891.8%
 
ValueCountFrequency (%) 
101650.1%
 
10536< 0.1%
 
1113570.4%
 
11222< 0.1%
 
118870.1%
 
1192360.3%
 
12123< 0.1%
 
12229< 0.1%
 
12334< 0.1%
 
124930.1%
 
ValueCountFrequency (%) 
21983< 0.1%
 
219722< 0.1%
 
2196470.1%
 
21891580.2%
 
21871620.2%
 
2185860.1%
 
2183760.1%
 
21771760.2%
 
21751100.1%
 
2173560.1%
 

Part 1-2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size680.3 KiB
1
51045 
2
36022 
ValueCountFrequency (%) 
15104558.6%
 
23602241.4%
 
2020-12-12T18:01:08.808480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:01:08.854520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:08.903562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
15104558.6%
 
23602241.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number87067100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
15104558.6%
 
23602241.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Common87067100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
15104558.6%
 
23602241.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII87067100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
15104558.6%
 
23602241.4%
 

Crm Cd
Real number (ℝ≥0)

HIGH CORRELATION

Distinct126
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean511.8506323
Minimum110
Maximum956
Zeros0
Zeros (%)0.0%
Memory size680.3 KiB
2020-12-12T18:01:08.971621image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile230
Q1330
median510
Q3626
95-th percentile901
Maximum956
Range846
Interquartile range (IQR)296

Descriptive statistics

Standard deviation209.5586464
Coefficient of variation (CV)0.409413671
Kurtosis-0.8460755452
Mean511.8506323
Median Absolute Deviation (MAD)179
Skewness0.3962564076
Sum44565299
Variance43914.82628
MonotocityNot monotonic
2020-12-12T18:01:09.048687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
51085529.8%
 
62473688.5%
 
33061727.1%
 
31057066.6%
 
74056846.5%
 
44049865.7%
 
62649255.7%
 
42046515.3%
 
23045815.3%
 
74531463.6%
 
21030953.6%
 
35427223.1%
 
34124482.8%
 
33119852.3%
 
93019802.3%
 
44218312.1%
 
88812961.5%
 
23612631.5%
 
76112531.4%
 
90111731.3%
 
9468321.0%
 
4808230.9%
 
9006760.8%
 
9565970.7%
 
2205050.6%
 
Other values (101)881710.1%
 
ValueCountFrequency (%) 
1101200.1%
 
1131< 0.1%
 
1213000.3%
 
12229< 0.1%
 
21030953.6%
 
2205050.6%
 
23045815.3%
 
2311950.2%
 
235620.1%
 
23612631.5%
 
ValueCountFrequency (%) 
9565970.7%
 
9545< 0.1%
 
95141< 0.1%
 
95015< 0.1%
 
9499< 0.1%
 
9468321.0%
 
9444< 0.1%
 
94311< 0.1%
 
9421< 0.1%
 
9401930.2%
 

Crm Cd Desc
Categorical

HIGH CARDINALITY

Distinct126
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size680.3 KiB
VEHICLE - STOLEN
8552 
BATTERY - SIMPLE ASSAULT
7368 
BURGLARY FROM VEHICLE
6172 
BURGLARY
5706 
VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS)
5684 
Other values (121)
53585 
ValueCountFrequency (%) 
VEHICLE - STOLEN85529.8%
 
BATTERY - SIMPLE ASSAULT73688.5%
 
BURGLARY FROM VEHICLE61727.1%
 
BURGLARY57066.6%
 
VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS)56846.5%
 
THEFT PLAIN - PETTY ($950 & UNDER)49865.7%
 
INTIMATE PARTNER - SIMPLE ASSAULT49255.7%
 
THEFT FROM MOTOR VEHICLE - PETTY ($950 & UNDER)46515.3%
 
ASSAULT WITH DEADLY WEAPON, AGGRAVATED ASSAULT45815.3%
 
VANDALISM - MISDEAMEANOR ($399 OR UNDER)31463.6%
 
ROBBERY30953.6%
 
THEFT OF IDENTITY27223.1%
 
THEFT-GRAND ($950.01 & OVER)EXCPT,GUNS,FOWL,LIVESTK,PROD24482.8%
 
THEFT FROM MOTOR VEHICLE - GRAND ($400 AND OVER)19852.3%
 
CRIMINAL THREATS - NO WEAPON DISPLAYED19802.3%
 
SHOPLIFTING - PETTY THEFT ($950 & UNDER)18312.1%
 
TRESPASSING12961.5%
 
INTIMATE PARTNER - AGGRAVATED ASSAULT12631.5%
 
BRANDISH WEAPON12531.4%
 
VIOLATION OF RESTRAINING ORDER11731.3%
 
OTHER MISCELLANEOUS CRIME8321.0%
 
BIKE - STOLEN8230.9%
 
VIOLATION OF COURT ORDER6760.8%
 
LETTERS, LEWD - TELEPHONE CALLS, LEWD5970.7%
 
ATTEMPTED ROBBERY5050.6%
 
Other values (101)881710.1%
 
2020-12-12T18:01:09.144769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique8 ?
Unique (%)< 0.1%
2020-12-12T18:01:09.267375image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length56
Median length25
Mean length29.26847141
Min length5

Overview of Unicode Properties

Unique unicode characters42
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
31480712.4%
 
E2188388.6%
 
T1874027.4%
 
A1851157.3%
 
R1472185.8%
 
L1458245.7%
 
S1203704.7%
 
I1119364.4%
 
N1086524.3%
 
O1026764.0%
 
D735372.9%
 
H692782.7%
 
M674912.6%
 
U647432.5%
 
P600512.4%
 
V575972.3%
 
Y528092.1%
 
F518592.0%
 
-516162.0%
 
C483051.9%
 
G391291.5%
 
0332781.3%
 
B324511.3%
 
(265771.0%
 
)264791.0%
 
Other values (17)1502805.9%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter197109777.3%
 
Space Separator31480712.4%
 
Decimal Number835043.3%
 
Dash Punctuation516162.0%
 
Other Punctuation488731.9%
 
Open Punctuation265771.0%
 
Close Punctuation264791.0%
 
Currency Symbol253651.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E21883811.1%
 
T1874029.5%
 
A1851159.4%
 
R1472187.5%
 
L1458247.4%
 
S1203706.1%
 
I1119365.7%
 
N1086525.5%
 
O1026765.2%
 
D735373.7%
 
H692783.5%
 
M674913.4%
 
U647433.3%
 
P600513.0%
 
V575972.9%
 
Y528092.7%
 
F518592.6%
 
C483052.5%
 
G391292.0%
 
B324511.6%
 
W177170.9%
 
K36160.2%
 
X35210.2%
 
Z681< 0.1%
 
J281< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
314807100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-51616100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(26577100.0%
 

Most frequent Currency Symbol characters

ValueCountFrequency (%) 
$25365100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
03327839.9%
 
92077624.9%
 
51459917.5%
 
478409.4%
 
135414.2%
 
333704.0%
 
7900.1%
 
210< 0.1%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)26479100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,2416349.4%
 
&2042441.8%
 
.33186.8%
 
/9682.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin197109777.3%
 
Common57722122.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E21883811.1%
 
T1874029.5%
 
A1851159.4%
 
R1472187.5%
 
L1458247.4%
 
S1203706.1%
 
I1119365.7%
 
N1086525.5%
 
O1026765.2%
 
D735373.7%
 
H692783.5%
 
M674913.4%
 
U647433.3%
 
P600513.0%
 
V575972.9%
 
Y528092.7%
 
F518592.6%
 
C483052.5%
 
G391292.0%
 
B324511.6%
 
W177170.9%
 
K36160.2%
 
X35210.2%
 
Z681< 0.1%
 
J281< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
31480754.5%
 
-516168.9%
 
0332785.8%
 
(265774.6%
 
)264794.6%
 
$253654.4%
 
,241634.2%
 
9207763.6%
 
&204243.5%
 
5145992.5%
 
478401.4%
 
135410.6%
 
333700.6%
 
.33180.6%
 
/9680.2%
 
790< 0.1%
 
210< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2548318100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
31480712.4%
 
E2188388.6%
 
T1874027.4%
 
A1851157.3%
 
R1472185.8%
 
L1458245.7%
 
S1203704.7%
 
I1119364.4%
 
N1086524.3%
 
O1026764.0%
 
D735372.9%
 
H692782.7%
 
M674912.6%
 
U647432.5%
 
P600512.4%
 
V575972.3%
 
Y528092.1%
 
F518592.0%
 
-516162.0%
 
C483051.9%
 
G391291.5%
 
0332781.3%
 
B324511.3%
 
(265771.0%
 
)264791.0%
 
Other values (17)1502805.9%
 

Mocodes
Categorical

HIGH CARDINALITY
MISSING

Distinct38482
Distinct (%)50.7%
Missing11129
Missing (%)12.8%
Memory size680.3 KiB
0344
 
4243
0329
 
2629
1501
 
920
0344 1300
 
615
0329 1300
 
614
Other values (38477)
66917 
ValueCountFrequency (%) 
034442434.9%
 
032926293.0%
 
15019201.1%
 
0344 13006150.7%
 
0329 13006140.7%
 
03256110.7%
 
0344 18225250.6%
 
04164680.5%
 
0329 18224590.5%
 
20384230.5%
 
1822 03444140.5%
 
18223620.4%
 
0344 03943580.4%
 
1300 03443540.4%
 
1300 03293090.4%
 
0344 16062780.3%
 
1606 03442300.3%
 
0329 13072240.3%
 
0344 20321970.2%
 
04001910.2%
 
01001820.2%
 
1609 03441810.2%
 
1822 03291770.2%
 
20041770.2%
 
0344 03851760.2%
 
Other values (38457)6062169.6%
 
(Missing)1112912.8%
 
2020-12-12T18:01:09.429014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique34134 ?
Unique (%)44.9%
2020-12-12T18:01:09.508583image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length49
Median length14
Mean length14.71101565
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
028406222.2%
 
18874114.7%
 
116578412.9%
 
415253611.9%
 
214192511.1%
 
31240899.7%
 
9522684.1%
 
8479823.7%
 
6439343.4%
 
5275962.2%
 
n222581.7%
 
7185401.4%
 
a111290.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number105871682.7%
 
Space Separator18874114.7%
 
Lowercase Letter333872.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
028406226.8%
 
116578415.7%
 
415253614.4%
 
214192513.4%
 
312408911.7%
 
9522684.9%
 
8479824.5%
 
6439344.1%
 
5275962.6%
 
7185401.8%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
188741100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2225866.7%
 
a1112933.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common124745797.4%
 
Latin333872.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
028406222.8%
 
18874115.1%
 
116578413.3%
 
415253612.2%
 
214192511.4%
 
31240899.9%
 
9522684.2%
 
8479823.8%
 
6439343.5%
 
5275962.2%
 
7185401.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n2225866.7%
 
a1112933.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1280844100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
028406222.2%
 
18874114.7%
 
116578412.9%
 
415253611.9%
 
214192511.1%
 
31240899.7%
 
9522684.1%
 
8479823.7%
 
6439343.4%
 
5275962.2%
 
n222581.7%
 
7185401.4%
 
a111290.9%
 

Vict Age
Real number (ℝ≥0)

ZEROS

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.10884721
Minimum0
Maximum120
Zeros20917
Zeros (%)24.0%
Memory size680.3 KiB
2020-12-12T18:01:09.580645image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median31
Q346
95-th percentile65
Maximum120
Range120
Interquartile range (IQR)33

Descriptive statistics

Standard deviation21.71025747
Coefficient of variation (CV)0.7210590736
Kurtosis-0.7619669612
Mean30.10884721
Median Absolute Deviation (MAD)15
Skewness0.09728553752
Sum2621487
Variance471.3352796
MonotocityNot monotonic
2020-12-12T18:01:09.670222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02091724.0%
 
3020182.3%
 
2919362.2%
 
3518842.2%
 
2718452.1%
 
2818442.1%
 
3118222.1%
 
2617672.0%
 
3217322.0%
 
3416951.9%
 
3316831.9%
 
2516791.9%
 
2415721.8%
 
3615591.8%
 
2315311.8%
 
3815291.8%
 
3715261.8%
 
1915141.7%
 
3914061.6%
 
4013871.6%
 
5013441.5%
 
4113241.5%
 
2212831.5%
 
4312301.4%
 
4211511.3%
 
Other values (75)2788932.0%
 
ValueCountFrequency (%) 
02091724.0%
 
241< 0.1%
 
341< 0.1%
 
4470.1%
 
5650.1%
 
6450.1%
 
7470.1%
 
8510.1%
 
9610.1%
 
10760.1%
 
ValueCountFrequency (%) 
1201< 0.1%
 
99440.1%
 
988< 0.1%
 
975< 0.1%
 
9613< 0.1%
 
9512< 0.1%
 
9411< 0.1%
 
9313< 0.1%
 
9212< 0.1%
 
9123< 0.1%
 

Vict Sex
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing10602
Missing (%)12.2%
Memory size680.3 KiB
M
37447 
F
31566 
X
7441 
H
 
11
ValueCountFrequency (%) 
M3744743.0%
 
F3156636.3%
 
X74418.5%
 
H11< 0.1%
 
(Missing)1060212.2%
 
2020-12-12T18:01:09.757297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:01:09.806839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:09.901921image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.243536587
Min length1

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
M3744734.6%
 
F3156629.2%
 
n2120419.6%
 
a106029.8%
 
X74416.9%
 
H11< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter7646570.6%
 
Lowercase Letter3180629.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M3744749.0%
 
F3156641.3%
 
X74419.7%
 
H11< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2120466.7%
 
a1060233.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin108271100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
M3744734.6%
 
F3156629.2%
 
n2120419.6%
 
a106029.8%
 
X74416.9%
 
H11< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII108271100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
M3744734.6%
 
F3156629.2%
 
n2120419.6%
 
a106029.8%
 
X74416.9%
 
H11< 0.1%
 

Vict Descent
Categorical

MISSING

Distinct19
Distinct (%)< 0.1%
Missing10604
Missing (%)12.2%
Memory size680.3 KiB
H
27417 
W
18452 
B
12069 
X
8307 
O
7251 
Other values (14)
2967 
ValueCountFrequency (%) 
H2741731.5%
 
W1845221.2%
 
B1206913.9%
 
X83079.5%
 
O72518.3%
 
A20832.4%
 
K3570.4%
 
F1610.2%
 
C1570.2%
 
J730.1%
 
I40< 0.1%
 
V39< 0.1%
 
Z15< 0.1%
 
P12< 0.1%
 
U12< 0.1%
 
G8< 0.1%
 
S6< 0.1%
 
L2< 0.1%
 
D2< 0.1%
 
(Missing)1060412.2%
 
2020-12-12T18:01:09.981990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:01:10.055553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.243582528
Min length1

Overview of Unicode Properties

Unique unicode characters21
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
H2741725.3%
 
n2120819.6%
 
W1845217.0%
 
B1206911.1%
 
a106049.8%
 
X83077.7%
 
O72516.7%
 
A20831.9%
 
K3570.3%
 
F1610.1%
 
C1570.1%
 
J730.1%
 
I40< 0.1%
 
V39< 0.1%
 
Z15< 0.1%
 
P12< 0.1%
 
U12< 0.1%
 
G8< 0.1%
 
S6< 0.1%
 
D2< 0.1%
 
L2< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter7646370.6%
 
Lowercase Letter3181229.4%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
H2741735.9%
 
W1845224.1%
 
B1206915.8%
 
X830710.9%
 
O72519.5%
 
A20832.7%
 
K3570.5%
 
F1610.2%
 
C1570.2%
 
J730.1%
 
I400.1%
 
V390.1%
 
Z15< 0.1%
 
P12< 0.1%
 
U12< 0.1%
 
G8< 0.1%
 
S6< 0.1%
 
D2< 0.1%
 
L2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n2120866.7%
 
a1060433.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin108275100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
H2741725.3%
 
n2120819.6%
 
W1845217.0%
 
B1206911.1%
 
a106049.8%
 
X83077.7%
 
O72516.7%
 
A20831.9%
 
K3570.3%
 
F1610.1%
 
C1570.1%
 
J730.1%
 
I40< 0.1%
 
V39< 0.1%
 
Z15< 0.1%
 
P12< 0.1%
 
U12< 0.1%
 
G8< 0.1%
 
S6< 0.1%
 
D2< 0.1%
 
L2< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII108275100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
H2741725.3%
 
n2120819.6%
 
W1845217.0%
 
B1206911.1%
 
a106049.8%
 
X83077.7%
 
O72516.7%
 
A20831.9%
 
K3570.3%
 
F1610.1%
 
C1570.1%
 
J730.1%
 
I40< 0.1%
 
V39< 0.1%
 
Z15< 0.1%
 
P12< 0.1%
 
U12< 0.1%
 
G8< 0.1%
 
S6< 0.1%
 
D2< 0.1%
 
L2< 0.1%
 

Premis Cd
Real number (ℝ≥0)

Distinct282
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean294.3333218
Minimum101
Maximum971
Zeros0
Zeros (%)0.0%
Memory size680.3 KiB
2020-12-12T18:01:10.141127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1101
median203
Q3501
95-th percentile707
Maximum971
Range870
Interquartile range (IQR)400

Descriptive statistics

Standard deviation212.7184964
Coefficient of variation (CV)0.7227129266
Kurtosis-0.9839653777
Mean294.3333218
Median Absolute Deviation (MAD)102
Skewness0.594457802
Sum25626425
Variance45249.15872
MonotocityNot monotonic
2020-12-12T18:01:10.221196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1012198125.2%
 
5011428316.4%
 
5021013311.6%
 
10865497.5%
 
10241864.8%
 
20339004.5%
 
12230023.4%
 
10418432.1%
 
70717292.0%
 
21012261.4%
 
12310481.2%
 
4028771.0%
 
4047440.9%
 
1036840.8%
 
1096150.7%
 
7105990.7%
 
3015800.7%
 
4035800.7%
 
1215720.7%
 
4064970.6%
 
4014910.6%
 
5034520.5%
 
2213660.4%
 
5043570.4%
 
1193520.4%
 
Other values (257)942010.8%
 
ValueCountFrequency (%) 
1012198125.2%
 
10241864.8%
 
1036840.8%
 
10418432.1%
 
1051< 0.1%
 
1064< 0.1%
 
10730< 0.1%
 
10865497.5%
 
1096150.7%
 
11034< 0.1%
 
ValueCountFrequency (%) 
9712< 0.1%
 
9704< 0.1%
 
9691< 0.1%
 
9683< 0.1%
 
9671< 0.1%
 
96611< 0.1%
 
9633< 0.1%
 
9626< 0.1%
 
9611< 0.1%
 
9583< 0.1%
 

Premis Desc
Categorical

HIGH CARDINALITY

Distinct280
Distinct (%)0.3%
Missing38
Missing (%)< 0.1%
Memory size680.3 KiB
STREET
21981 
SINGLE FAMILY DWELLING
14283 
MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)
10133 
PARKING LOT
6549 
SIDEWALK
4186 
Other values (275)
29897 
ValueCountFrequency (%) 
STREET2198125.2%
 
SINGLE FAMILY DWELLING1428316.4%
 
MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)1013311.6%
 
PARKING LOT65497.5%
 
SIDEWALK41864.8%
 
OTHER BUSINESS39004.5%
 
VEHICLE, PASSENGER/TRUCK30023.4%
 
DRIVEWAY18432.1%
 
GARAGE/CARPORT17292.0%
 
RESTAURANT/FAST FOOD12261.4%
 
PARKING UNDERGROUND/BUILDING10481.2%
 
MARKET8771.0%
 
DEPARTMENT STORE7440.9%
 
ALLEY6840.8%
 
PARK/PLAYGROUND6150.7%
 
OTHER PREMISE5990.7%
 
DRUG STORE5800.7%
 
GAS STATION5800.7%
 
YARD (RESIDENTIAL/BUSINESS)5720.7%
 
OTHER STORE4970.6%
 
MINI-MART4910.6%
 
HOTEL4520.5%
 
PUBLIC STORAGE3660.4%
 
OTHER RESIDENCE3570.4%
 
PORCH, RESIDENTIAL3520.4%
 
Other values (255)938310.8%
 
2020-12-12T18:01:10.311774image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique31 ?
Unique (%)< 0.1%
2020-12-12T18:01:10.399850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length63
Median length14
Mean length17.34242595
Min length3

Overview of Unicode Properties

Unique unicode characters47
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
E17239211.4%
 
T1368479.1%
 
L1259178.3%
 
I1131967.5%
 
1055617.0%
 
N927846.1%
 
R830135.5%
 
S785705.2%
 
A783745.2%
 
G601314.0%
 
D525293.5%
 
U496943.3%
 
M430612.9%
 
P409842.7%
 
O382192.5%
 
W315392.1%
 
C292911.9%
 
,255341.7%
 
Y208411.4%
 
F204431.4%
 
K170651.1%
 
H169421.1%
 
(124820.8%
 
)122810.8%
 
-117270.8%
 
Other values (22)405362.7%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter132772487.9%
 
Space Separator1055617.0%
 
Other Punctuation387122.6%
 
Open Punctuation124820.8%
 
Close Punctuation122810.8%
 
Dash Punctuation117270.8%
 
Decimal Number13520.1%
 
Lowercase Letter114< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E17239213.0%
 
T13684710.3%
 
L1259179.5%
 
I1131968.5%
 
N927847.0%
 
R830136.3%
 
S785705.9%
 
A783745.9%
 
G601314.5%
 
D525294.0%
 
U496943.7%
 
M430613.2%
 
P409843.1%
 
O382192.9%
 
W315392.4%
 
C292912.2%
 
Y208411.6%
 
F204431.5%
 
K170651.3%
 
H169421.3%
 
X103640.8%
 
B88440.7%
 
V58630.4%
 
J448< 0.1%
 
Q370< 0.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
105561100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-11727100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(12482100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,2553466.0%
 
/1163630.1%
 
.4611.2%
 
*4591.2%
 
'4301.1%
 
&1680.4%
 
:240.1%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)12281100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
042431.4%
 
341030.3%
 
939529.2%
 
7564.1%
 
1322.4%
 
8211.6%
 
270.5%
 
470.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n7666.7%
 
a3833.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin132783887.9%
 
Common18211512.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E17239213.0%
 
T13684710.3%
 
L1259179.5%
 
I1131968.5%
 
N927847.0%
 
R830136.3%
 
S785705.9%
 
A783745.9%
 
G601314.5%
 
D525294.0%
 
U496943.7%
 
M430613.2%
 
P409843.1%
 
O382192.9%
 
W315392.4%
 
C292912.2%
 
Y208411.6%
 
F204431.5%
 
K170651.3%
 
H169421.3%
 
X103640.8%
 
B88440.7%
 
V58630.4%
 
J448< 0.1%
 
Q370< 0.1%
 
Other values (3)117< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
10556158.0%
 
,2553414.0%
 
(124826.9%
 
)122816.7%
 
-117276.4%
 
/116366.4%
 
.4610.3%
 
*4590.3%
 
'4300.2%
 
04240.2%
 
34100.2%
 
93950.2%
 
&1680.1%
 
756< 0.1%
 
132< 0.1%
 
:24< 0.1%
 
821< 0.1%
 
27< 0.1%
 
47< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1509953100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
E17239211.4%
 
T1368479.1%
 
L1259178.3%
 
I1131967.5%
 
1055617.0%
 
N927846.1%
 
R830135.5%
 
S785705.2%
 
A783745.2%
 
G601314.0%
 
D525293.5%
 
U496943.3%
 
M430612.9%
 
P409842.7%
 
O382192.5%
 
W315392.1%
 
C292911.9%
 
,255341.7%
 
Y208411.4%
 
F204431.4%
 
K170651.1%
 
H169421.1%
 
(124820.8%
 
)122810.8%
 
-117270.8%
 
Other values (22)405362.7%
 

Weapon Used Cd
Real number (ℝ≥0)

MISSING

Distinct72
Distinct (%)0.2%
Missing55243
Missing (%)63.4%
Infinite0
Infinite (%)0.0%
Mean371.2318376
Minimum101
Maximum516
Zeros0
Zeros (%)0.0%
Memory size680.3 KiB
2020-12-12T18:01:10.487425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1400
median400
Q3400
95-th percentile511
Maximum516
Range415
Interquartile range (IQR)0

Descriptive statistics

Standard deviation116.3588388
Coefficient of variation (CV)0.3134398158
Kurtosis0.3958756987
Mean371.2318376
Median Absolute Deviation (MAD)0
Skewness-1.099194305
Sum11814082
Variance13539.37936
MonotocityNot monotonic
2020-12-12T18:01:10.568995image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4001772420.4%
 
50035274.1%
 
51123902.7%
 
10214901.7%
 
2006450.7%
 
1096020.7%
 
2075350.6%
 
1063970.5%
 
3063040.3%
 
3072970.3%
 
5122680.3%
 
3122470.3%
 
2122400.3%
 
3042250.3%
 
2042100.2%
 
3082040.2%
 
2051830.2%
 
1011820.2%
 
3021720.2%
 
2011710.2%
 
3111470.2%
 
1141410.2%
 
1131320.2%
 
2151060.1%
 
2231000.1%
 
Other values (47)11851.4%
 
(Missing)5524363.4%
 
ValueCountFrequency (%) 
1011820.2%
 
10214901.7%
 
10341< 0.1%
 
10442< 0.1%
 
1054< 0.1%
 
1063970.5%
 
107620.1%
 
1083< 0.1%
 
1096020.7%
 
1104< 0.1%
 
ValueCountFrequency (%) 
5167< 0.1%
 
515930.1%
 
51420< 0.1%
 
51334< 0.1%
 
5122680.3%
 
51123902.7%
 
51011< 0.1%
 
5096< 0.1%
 
5082< 0.1%
 
5075< 0.1%
 

Weapon Desc
Categorical

HIGH CARDINALITY
MISSING

Distinct72
Distinct (%)0.2%
Missing55243
Missing (%)63.4%
Memory size680.3 KiB
STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)
17724 
UNKNOWN WEAPON/OTHER WEAPON
3527 
VERBAL THREAT
2390 
HAND GUN
 
1490
KNIFE WITH BLADE 6INCHES OR LESS
 
645
Other values (67)
6048 
ValueCountFrequency (%) 
STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)1772420.4%
 
UNKNOWN WEAPON/OTHER WEAPON35274.1%
 
VERBAL THREAT23902.7%
 
HAND GUN14901.7%
 
KNIFE WITH BLADE 6INCHES OR LESS6450.7%
 
SEMI-AUTOMATIC PISTOL6020.7%
 
OTHER KNIFE5350.6%
 
UNKNOWN FIREARM3970.5%
 
ROCK/THROWN OBJECT3040.3%
 
VEHICLE2970.3%
 
MACE/PEPPER SPRAY2680.3%
 
PIPE/METAL PIPE2470.3%
 
BOTTLE2400.3%
 
CLUB/BAT2250.3%
 
FOLDING KNIFE2100.2%
 
STICK2040.2%
 
KITCHEN KNIFE1830.2%
 
REVOLVER1820.2%
 
BLUNT INSTRUMENT1720.2%
 
KNIFE WITH BLADE OVER 6 INCHES IN LENGTH1710.2%
 
HAMMER1470.2%
 
AIR PISTOL/REVOLVER/RIFLE/BB GUN1410.2%
 
SIMULATED GUN1320.2%
 
MACHETE1060.1%
 
UNKNOWN TYPE CUTTING INSTRUMENT1000.1%
 
Other values (47)11851.4%
 
(Missing)5524363.4%
 
2020-12-12T18:01:10.666579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2020-12-12T18:01:10.751652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length46
Median length3
Mean length14.10441384
Min length3

Overview of Unicode Properties

Unique unicode characters42
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
12630210.3%
 
n1104869.0%
 
O906557.4%
 
R852926.9%
 
E806076.6%
 
T693535.6%
 
N633335.2%
 
S586014.8%
 
F559964.6%
 
a552434.5%
 
A534984.4%
 
I448943.7%
 
D384133.1%
 
,354482.9%
 
H289562.4%
 
L251842.1%
 
B228371.9%
 
C222781.8%
 
M209971.7%
 
G203351.7%
 
-183351.5%
 
Y182071.5%
 
(177311.4%
 
)177311.4%
 
W123361.0%
 
Other values (17)349812.8%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter84063968.5%
 
Lowercase Letter16572913.5%
 
Space Separator12630210.3%
 
Other Punctuation407123.3%
 
Dash Punctuation183351.5%
 
Open Punctuation177311.4%
 
Close Punctuation177311.4%
 
Decimal Number8500.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O9065510.8%
 
R8529210.1%
 
E806079.6%
 
T693538.3%
 
N633337.5%
 
S586017.0%
 
F559966.7%
 
A534986.4%
 
I448945.3%
 
D384134.6%
 
H289563.4%
 
L251843.0%
 
B228372.7%
 
C222782.7%
 
M209972.5%
 
G203352.4%
 
Y182072.2%
 
W123361.5%
 
P102011.2%
 
U78160.9%
 
K67300.8%
 
V36210.4%
 
J353< 0.1%
 
X96< 0.1%
 
Z32< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-18335100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
126302100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(17731100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,3544887.1%
 
/525712.9%
 
&7< 0.1%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)17731100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n11048666.7%
 
a5524333.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
681696.0%
 
480.9%
 
780.9%
 
970.8%
 
370.8%
 
130.4%
 
010.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin100636881.9%
 
Common22166118.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n11048611.0%
 
O906559.0%
 
R852928.5%
 
E806078.0%
 
T693536.9%
 
N633336.3%
 
S586015.8%
 
F559965.6%
 
a552435.5%
 
A534985.3%
 
I448944.5%
 
D384133.8%
 
H289562.9%
 
L251842.5%
 
B228372.3%
 
C222782.2%
 
M209972.1%
 
G203352.0%
 
Y182071.8%
 
W123361.2%
 
P102011.0%
 
U78160.8%
 
K67300.7%
 
V36210.4%
 
J353< 0.1%
 
Other values (3)146< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
12630257.0%
 
,3544816.0%
 
-183358.3%
 
(177318.0%
 
)177318.0%
 
/52572.4%
 
68160.4%
 
48< 0.1%
 
78< 0.1%
 
&7< 0.1%
 
97< 0.1%
 
37< 0.1%
 
13< 0.1%
 
01< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1228029100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
12630210.3%
 
n1104869.0%
 
O906557.4%
 
R852926.9%
 
E806076.6%
 
T693535.6%
 
N633335.2%
 
S586014.8%
 
F559964.6%
 
a552434.5%
 
A534984.4%
 
I448943.7%
 
D384133.1%
 
,354482.9%
 
H289562.4%
 
L251842.1%
 
B228371.9%
 
C222781.8%
 
M209971.7%
 
G203351.7%
 
-183351.5%
 
Y182071.5%
 
(177311.4%
 
)177311.4%
 
W123361.0%
 
Other values (17)349812.8%
 

Status
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size680.3 KiB
IC
73311 
AO
 
6775
AA
 
6529
JA
 
359
JO
 
93
ValueCountFrequency (%) 
IC7331184.2%
 
AO67757.8%
 
AA65297.5%
 
JA3590.4%
 
JO930.1%
 
2020-12-12T18:01:10.826717image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:01:10.875759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:10.933309image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
I7331142.1%
 
C7331142.1%
 
A2019211.6%
 
O68683.9%
 
J4520.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter174134100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I7331142.1%
 
C7331142.1%
 
A2019211.6%
 
O68683.9%
 
J4520.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin174134100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
I7331142.1%
 
C7331142.1%
 
A2019211.6%
 
O68683.9%
 
J4520.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII174134100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
I7331142.1%
 
C7331142.1%
 
A2019211.6%
 
O68683.9%
 
J4520.3%
 

Status Desc
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size680.3 KiB
Invest Cont
73311 
Adult Other
 
6775
Adult Arrest
 
6529
Juv Arrest
 
359
Juv Other
 
93
ValueCountFrequency (%) 
Invest Cont7331184.2%
 
Adult Other67757.8%
 
Adult Arrest65297.5%
 
Juv Arrest3590.4%
 
Juv Other930.1%
 
2020-12-12T18:01:11.000867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:01:11.049909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:11.118968image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length11
Mean length11.06872868
Min length9

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t17368218.0%
 
n14662215.2%
 
870679.0%
 
e870679.0%
 
s801998.3%
 
v737637.7%
 
I733117.6%
 
C733117.6%
 
o733117.6%
 
r206442.1%
 
A201922.1%
 
u137561.4%
 
d133041.4%
 
l133041.4%
 
O68680.7%
 
h68680.7%
 
J452< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter70252072.9%
 
Uppercase Letter17413418.1%
 
Space Separator870679.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I7331142.1%
 
C7331142.1%
 
A2019211.6%
 
O68683.9%
 
J4520.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t17368224.7%
 
n14662220.9%
 
e8706712.4%
 
s8019911.4%
 
v7376310.5%
 
o7331110.4%
 
r206442.9%
 
u137562.0%
 
d133041.9%
 
l133041.9%
 
h68681.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
87067100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin87665491.0%
 
Common870679.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t17368219.8%
 
n14662216.7%
 
e870679.9%
 
s801999.1%
 
v737638.4%
 
I733118.4%
 
C733118.4%
 
o733118.4%
 
r206442.4%
 
A201922.3%
 
u137561.6%
 
d133041.5%
 
l133041.5%
 
O68680.8%
 
h68680.8%
 
J4520.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
87067100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII963721100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t17368218.0%
 
n14662215.2%
 
870679.0%
 
e870679.0%
 
s801998.3%
 
v737637.7%
 
I733117.6%
 
C733117.6%
 
o733117.6%
 
r206442.1%
 
A201922.1%
 
u137561.4%
 
d133041.4%
 
l133041.4%
 
O68680.7%
 
h68680.7%
 
J452< 0.1%
 

Crm Cd 1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct126
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean511.5546023
Minimum110
Maximum956
Zeros0
Zeros (%)0.0%
Memory size680.3 KiB
2020-12-12T18:01:11.194533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile230
Q1330
median510
Q3626
95-th percentile901
Maximum956
Range846
Interquartile range (IQR)296

Descriptive statistics

Standard deviation209.31733
Coefficient of variation (CV)0.409178862
Kurtosis-0.8408883298
Mean511.5546023
Median Absolute Deviation (MAD)179
Skewness0.3975318199
Sum44539013
Variance43813.74464
MonotocityNot monotonic
2020-12-12T18:01:11.272100image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
51085549.8%
 
62474018.5%
 
33061747.1%
 
31057056.6%
 
74056946.5%
 
44049885.7%
 
62649605.7%
 
42046525.3%
 
23045815.3%
 
74531533.6%
 
21030963.6%
 
35427233.1%
 
34124532.8%
 
33119872.3%
 
93019692.3%
 
44218312.1%
 
88812971.5%
 
23612641.5%
 
76111831.4%
 
90111731.3%
 
9468321.0%
 
4808230.9%
 
9006770.8%
 
9565970.7%
 
2205050.6%
 
Other values (101)879410.1%
 
ValueCountFrequency (%) 
1101200.1%
 
1131< 0.1%
 
1213000.3%
 
12229< 0.1%
 
21030963.6%
 
2205050.6%
 
23045815.3%
 
2311950.2%
 
235620.1%
 
23612641.5%
 
ValueCountFrequency (%) 
9565970.7%
 
9545< 0.1%
 
95141< 0.1%
 
95014< 0.1%
 
9499< 0.1%
 
9468321.0%
 
9444< 0.1%
 
94311< 0.1%
 
9421< 0.1%
 
9401930.2%
 

Crm Cd 2
Real number (ℝ≥0)

MISSING

Distinct78
Distinct (%)1.1%
Missing79995
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean954.4346719
Minimum210
Maximum999
Zeros0
Zeros (%)0.0%
Memory size680.3 KiB
2020-12-12T18:01:11.354671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum210
5-th percentile626
Q1998
median998
Q3998
95-th percentile998
Maximum999
Range789
Interquartile range (IQR)0

Descriptive statistics

Standard deviation118.6656591
Coefficient of variation (CV)0.1243308344
Kurtosis11.31854968
Mean954.4346719
Median Absolute Deviation (MAD)0
Skewness-3.289958947
Sum6749762
Variance14081.53865
MonotocityNot monotonic
2020-12-12T18:01:11.441246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
99857446.6%
 
9303290.4%
 
6261460.2%
 
761930.1%
 
480760.1%
 
860730.1%
 
740650.1%
 
946560.1%
 
624560.1%
 
910520.1%
 
812500.1%
 
745470.1%
 
81519< 0.1%
 
82017< 0.1%
 
90114< 0.1%
 
23612< 0.1%
 
44012< 0.1%
 
88810< 0.1%
 
64910< 0.1%
 
51010< 0.1%
 
8218< 0.1%
 
8138< 0.1%
 
2308< 0.1%
 
5207< 0.1%
 
4427< 0.1%
 
Other values (53)1430.2%
 
(Missing)7999591.9%
 
ValueCountFrequency (%) 
2101< 0.1%
 
2202< 0.1%
 
2308< 0.1%
 
23612< 0.1%
 
2513< 0.1%
 
3106< 0.1%
 
3303< 0.1%
 
3311< 0.1%
 
3416< 0.1%
 
3431< 0.1%
 
ValueCountFrequency (%) 
9996< 0.1%
 
99857446.6%
 
9975< 0.1%
 
9961< 0.1%
 
9904< 0.1%
 
9803< 0.1%
 
9721< 0.1%
 
9511< 0.1%
 
9501< 0.1%
 
946560.1%
 

Crm Cd 3
Real number (ℝ≥0)

MISSING

Distinct11
Distinct (%)4.8%
Missing86837
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean981.3652174
Minimum626
Maximum999
Zeros0
Zeros (%)0.0%
Memory size680.3 KiB
2020-12-12T18:01:11.524317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum626
5-th percentile919
Q1998
median998
Q3998
95-th percentile998
Maximum999
Range373
Interquartile range (IQR)0

Descriptive statistics

Standard deviation59.03944806
Coefficient of variation (CV)0.06016052639
Kurtosis20.56811815
Mean981.3652174
Median Absolute Deviation (MAD)0
Skewness-4.425133524
Sum225714
Variance3485.656427
MonotocityNot monotonic
2020-12-12T18:01:11.587372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
9982010.2%
 
93014< 0.1%
 
6263< 0.1%
 
7403< 0.1%
 
7612< 0.1%
 
9102< 0.1%
 
9991< 0.1%
 
9971< 0.1%
 
9901< 0.1%
 
8601< 0.1%
 
8101< 0.1%
 
(Missing)8683799.7%
 
ValueCountFrequency (%) 
6263< 0.1%
 
7403< 0.1%
 
7612< 0.1%
 
8101< 0.1%
 
8601< 0.1%
 
9102< 0.1%
 
93014< 0.1%
 
9901< 0.1%
 
9971< 0.1%
 
9982010.2%
 
ValueCountFrequency (%) 
9991< 0.1%
 
9982010.2%
 
9971< 0.1%
 
9901< 0.1%
 
93014< 0.1%
 
9102< 0.1%
 
8601< 0.1%
 
8101< 0.1%
 
7612< 0.1%
 
7403< 0.1%
 

Crm Cd 4
Categorical

MISSING

Distinct1
Distinct (%)12.5%
Missing87059
Missing (%)> 99.9%
Memory size680.3 KiB
998
ValueCountFrequency (%) 
9988< 0.1%
 
(Missing)87059> 99.9%
 
2020-12-12T18:01:11.657932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T18:01:11.697967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:11.743006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length3
Mean length3.000183767
Min length3

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n17411866.7%
 
a8705933.3%
 
916< 0.1%
 
88< 0.1%
 
.8< 0.1%
 
08< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter261177> 99.9%
 
Decimal Number32< 0.1%
 
Other Punctuation8< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n17411866.7%
 
a8705933.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
91650.0%
 
8825.0%
 
0825.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.8100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin261177> 99.9%
 
Common40< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n17411866.7%
 
a8705933.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
91640.0%
 
8820.0%
 
.820.0%
 
0820.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII261217100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n17411866.7%
 
a8705933.3%
 
916< 0.1%
 
88< 0.1%
 
.8< 0.1%
 
08< 0.1%
 

LOCATION
Categorical

HIGH CARDINALITY

Distinct28410
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Memory size680.3 KiB
800 N ALAMEDA ST
 
171
7TH
 
167
6TH ST
 
152
6TH
 
145
HOLLYWOOD
 
143
Other values (28405)
86289 
ValueCountFrequency (%) 
800 N ALAMEDA ST1710.2%
 
7TH1670.2%
 
6TH ST1520.2%
 
6TH1450.2%
 
HOLLYWOOD1430.2%
 
7TH ST1420.2%
 
3RD ST1370.2%
 
BROADWAY1210.1%
 
5TH1200.1%
 
VERMONT AV1170.1%
 
9300 TAMPA AV1150.1%
 
FIGUEROA ST1070.1%
 
FIGUEROA1020.1%
 
WESTERN AV1010.1%
 
500 S SAN PEDRO ST960.1%
 
700 W 7TH ST950.1%
 
SUNSET940.1%
 
SHERMAN WY910.1%
 
MAIN ST880.1%
 
WILSHIRE870.1%
 
8TH ST830.1%
 
VERMONT830.1%
 
3RD800.1%
 
VAN NUYS BL760.1%
 
10200 SANTA MONICA BL730.1%
 
Other values (28385)8428196.8%
 
2020-12-12T18:01:11.883626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique13416 ?
Unique (%)15.4%
2020-12-12T18:01:11.975706image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length39
Mean length35.37703148
Min length1

Overview of Unicode Properties

Unique unicode characters37
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
201053865.3%
 
01540705.0%
 
A845902.7%
 
S684912.2%
 
T668022.2%
 
E612832.0%
 
N556261.8%
 
L538451.7%
 
R492931.6%
 
O462691.5%
 
1405211.3%
 
V376271.2%
 
I306751.0%
 
D258180.8%
 
B256930.8%
 
H251900.8%
 
W238000.8%
 
2193950.6%
 
C186490.6%
 
M169070.5%
 
3156380.5%
 
5152210.5%
 
4152070.5%
 
6148420.5%
 
7136700.4%
 
Other values (12)905122.9%
 

Most occurring categories

ValueCountFrequency (%) 
Space Separator201053865.3%
 
Uppercase Letter75706524.6%
 
Decimal Number31256910.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
015407049.3%
 
14052113.0%
 
2193956.2%
 
3156385.0%
 
5152214.9%
 
4152074.9%
 
6148424.7%
 
7136704.4%
 
8132374.2%
 
9107683.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2010538100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A8459011.2%
 
S684919.0%
 
T668028.8%
 
E612838.1%
 
N556267.3%
 
L538457.1%
 
R492936.5%
 
O462696.1%
 
V376275.0%
 
I306754.1%
 
D258183.4%
 
B256933.4%
 
H251903.3%
 
W238003.1%
 
C186492.5%
 
M169072.2%
 
U136111.8%
 
Y125941.7%
 
P114411.5%
 
G112811.5%
 
F79011.0%
 
K60550.8%
 
X12820.2%
 
J12310.2%
 
Z9800.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common232310775.4%
 
Latin75706524.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
201053886.5%
 
01540706.6%
 
1405211.7%
 
2193950.8%
 
3156380.7%
 
5152210.7%
 
4152070.7%
 
6148420.6%
 
7136700.6%
 
8132370.6%
 
9107680.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A8459011.2%
 
S684919.0%
 
T668028.8%
 
E612838.1%
 
N556267.3%
 
L538457.1%
 
R492936.5%
 
O462696.1%
 
V376275.0%
 
I306754.1%
 
D258183.4%
 
B256933.4%
 
H251903.3%
 
W238003.1%
 
C186492.5%
 
M169072.2%
 
U136111.8%
 
Y125941.7%
 
P114411.5%
 
G112811.5%
 
F79011.0%
 
K60550.8%
 
X12820.2%
 
J12310.2%
 
Z9800.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3080172100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
201053865.3%
 
01540705.0%
 
A845902.7%
 
S684912.2%
 
T668022.2%
 
E612832.0%
 
N556261.8%
 
L538451.7%
 
R492931.6%
 
O462691.5%
 
1405211.3%
 
V376271.2%
 
I306751.0%
 
D258180.8%
 
B256930.8%
 
H251900.8%
 
W238000.8%
 
2193950.6%
 
C186490.6%
 
M169070.5%
 
3156380.5%
 
5152210.5%
 
4152070.5%
 
6148420.5%
 
7136700.4%
 
Other values (12)905122.9%
 

Cross Street
Categorical

HIGH CARDINALITY
MISSING

Distinct3533
Distinct (%)22.7%
Missing71476
Missing (%)82.1%
Memory size680.3 KiB
BROADWAY
 
289
FIGUEROA
 
179
FIGUEROA ST
 
153
VERMONT AV
 
149
SAN PEDRO
 
143
Other values (3528)
14678 
ValueCountFrequency (%) 
BROADWAY2890.3%
 
FIGUEROA1790.2%
 
FIGUEROA ST1530.2%
 
VERMONT AV1490.2%
 
SAN PEDRO1430.2%
 
WESTERN AV1430.2%
 
MAIN ST1400.2%
 
VERMONT1210.1%
 
WESTERN960.1%
 
SUNSET940.1%
 
AVALON BL910.1%
 
CENTRAL AV860.1%
 
AVALON800.1%
 
HOOVER ST770.1%
 
WILSHIRE750.1%
 
OLYMPIC750.1%
 
SAN PEDRO ST730.1%
 
LOS ANGELES730.1%
 
MAIN700.1%
 
7TH ST690.1%
 
GRAND650.1%
 
6TH ST630.1%
 
SHERMAN WY620.1%
 
GRAND AV620.1%
 
3RD ST610.1%
 
Other values (3508)1300214.9%
 
(Missing)7147682.1%
 
2020-12-12T18:01:12.086801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1817 ?
Unique (%)11.7%
2020-12-12T18:01:12.177379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length34
Median length3
Mean length6.018123973
Min length1

Overview of Unicode Properties

Unique unicode characters39
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
18630335.6%
 
n14295227.3%
 
a7147613.6%
 
A136312.6%
 
E104912.0%
 
T95541.8%
 
N94691.8%
 
S90051.7%
 
O86901.7%
 
R85571.6%
 
L77331.5%
 
I56031.1%
 
V48980.9%
 
D40190.8%
 
H39930.8%
 
C32200.6%
 
M31890.6%
 
B30280.6%
 
W27540.5%
 
G23320.4%
 
U21490.4%
 
P20650.4%
 
Y20280.4%
 
F16910.3%
 
K10620.2%
 
Other values (14)40880.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter21442840.9%
 
Space Separator18630335.6%
 
Uppercase Letter11989722.9%
 
Decimal Number33520.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n14295266.7%
 
a7147633.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A1363111.4%
 
E104918.8%
 
T95548.0%
 
N94697.9%
 
S90057.5%
 
O86907.2%
 
R85577.1%
 
L77336.4%
 
I56034.7%
 
V48984.1%
 
D40193.4%
 
H39933.3%
 
C32202.7%
 
M31892.7%
 
B30282.5%
 
W27542.3%
 
G23321.9%
 
U21491.8%
 
P20651.7%
 
Y20281.7%
 
F16911.4%
 
K10620.9%
 
X2960.2%
 
J2570.2%
 
Z1720.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
160618.1%
 
238411.5%
 
536811.0%
 
434310.2%
 
83159.4%
 
73139.3%
 
33079.2%
 
62908.7%
 
02196.5%
 
92076.2%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
186303100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin33432563.8%
 
Common18965536.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n14295242.8%
 
a7147621.4%
 
A136314.1%
 
E104913.1%
 
T95542.9%
 
N94692.8%
 
S90052.7%
 
O86902.6%
 
R85572.6%
 
L77332.3%
 
I56031.7%
 
V48981.5%
 
D40191.2%
 
H39931.2%
 
C32201.0%
 
M31891.0%
 
B30280.9%
 
W27540.8%
 
G23320.7%
 
U21490.6%
 
P20650.6%
 
Y20280.6%
 
F16910.5%
 
K10620.3%
 
X2960.1%
 
Other values (3)4400.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
18630398.2%
 
16060.3%
 
23840.2%
 
53680.2%
 
43430.2%
 
83150.2%
 
73130.2%
 
33070.2%
 
62900.2%
 
02190.1%
 
92070.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII523980100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
18630335.6%
 
n14295227.3%
 
a7147613.6%
 
A136312.6%
 
E104912.0%
 
T95541.8%
 
N94691.8%
 
S90051.7%
 
O86901.7%
 
R85571.6%
 
L77331.5%
 
I56031.1%
 
V48980.9%
 
D40190.8%
 
H39930.8%
 
C32200.6%
 
M31890.6%
 
B30280.6%
 
W27540.5%
 
G23320.4%
 
U21490.4%
 
P20650.4%
 
Y20280.4%
 
F16910.3%
 
K10620.2%
 
Other values (14)40880.8%
 

LAT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4401
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.794901
Minimum0
Maximum34.3293
Zeros715
Zeros (%)0.8%
Memory size680.3 KiB
2020-12-12T18:01:12.270960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.86299
Q134.0099
median34.0588
Q334.16755
95-th percentile34.2544
Maximum34.3293
Range34.3293
Interquartile range (IQR)0.15765

Descriptive statistics

Standard deviation3.07723485
Coefficient of variation (CV)0.09105618775
Kurtosis116.4674611
Mean33.794901
Median Absolute Deviation (MAD)0.0666
Skewness-10.87665714
Sum2942420.645
Variance9.469374324
MonotocityNot monotonic
2020-12-12T18:01:12.354031image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
07150.8%
 
34.10164620.5%
 
34.20124340.5%
 
34.0983620.4%
 
34.18673470.4%
 
34.19392850.3%
 
34.16492620.3%
 
34.07612560.3%
 
34.20482460.3%
 
34.04732450.3%
 
34.19032240.3%
 
34.09442240.3%
 
34.19762170.2%
 
34.04672160.2%
 
34.1942110.2%
 
34.20852070.2%
 
34.19382030.2%
 
34.17942000.2%
 
34.09981970.2%
 
34.17221930.2%
 
34.05631930.2%
 
34.00731910.2%
 
34.09811830.2%
 
34.05591830.2%
 
34.15761770.2%
 
Other values (4376)8043492.4%
 
ValueCountFrequency (%) 
07150.8%
 
33.70652< 0.1%
 
33.7077< 0.1%
 
33.70711< 0.1%
 
33.70791< 0.1%
 
33.70873< 0.1%
 
33.70882< 0.1%
 
33.709610< 0.1%
 
33.71058< 0.1%
 
33.710610< 0.1%
 
ValueCountFrequency (%) 
34.32931< 0.1%
 
34.32872< 0.1%
 
34.32831< 0.1%
 
34.32771< 0.1%
 
34.32761< 0.1%
 
34.327516< 0.1%
 
34.32743< 0.1%
 
34.32721< 0.1%
 
34.32714< 0.1%
 
34.32661< 0.1%
 

LON
Real number (ℝ)

HIGH CORRELATION

Distinct4352
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-117.3820387
Minimum-118.6673
Maximum0
Zeros715
Zeros (%)0.8%
Memory size680.3 KiB
2020-12-12T18:01:12.441106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-118.6673
5-th percentile-118.56697
Q1-118.4296
median-118.3201
Q3-118.2739
95-th percentile-118.2145
Maximum0
Range118.6673
Interquartile range (IQR)0.1557

Descriptive statistics

Standard deviation10.68173304
Coefficient of variation (CV)-0.09099972328
Kurtosis116.7641899
Mean-117.3820387
Median Absolute Deviation (MAD)0.0635
Skewness10.89723627
Sum-10220101.96
Variance114.0994207
MonotocityNot monotonic
2020-12-12T18:01:12.525179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-118.27397750.9%
 
07150.8%
 
-118.28276610.8%
 
-118.30894560.5%
 
-118.27834420.5%
 
-118.28714320.5%
 
-118.26524230.5%
 
-118.29154160.5%
 
-118.46623430.4%
 
-118.3093370.4%
 
-118.29163290.4%
 
-118.26953180.4%
 
-118.27612560.3%
 
-118.28932400.3%
 
-118.37032390.3%
 
-118.27172370.3%
 
-118.44872260.3%
 
-118.30022230.3%
 
-118.28422230.3%
 
-118.53612230.3%
 
-118.2872190.3%
 
-118.25652140.2%
 
-118.26082010.2%
 
-118.45531870.2%
 
-118.30911850.2%
 
Other values (4327)7854790.2%
 
ValueCountFrequency (%) 
-118.66731< 0.1%
 
-118.66522< 0.1%
 
-118.66112< 0.1%
 
-118.66051< 0.1%
 
-118.66021< 0.1%
 
-118.66014< 0.1%
 
-118.6592< 0.1%
 
-118.65851< 0.1%
 
-118.65791< 0.1%
 
-118.65721< 0.1%
 
ValueCountFrequency (%) 
07150.8%
 
-118.15541< 0.1%
 
-118.15742< 0.1%
 
-118.15861< 0.1%
 
-118.15881< 0.1%
 
-118.15981< 0.1%
 
-118.161< 0.1%
 
-118.16041< 0.1%
 
-118.16121< 0.1%
 
-118.16131< 0.1%
 

Interactions

2020-12-12T18:00:53.380203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T18:01:04.722965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:04.787019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:04.853076image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:04.919634image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:04.985691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:05.048244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:05.115302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:05.211885image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:05.279443image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:05.354007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:05.417562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:05.623740image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T18:01:12.610252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T18:01:12.789907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T18:01:12.939535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T18:01:13.098172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T18:01:13.251804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T18:01:06.042600image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:06.654627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:07.113522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T18:01:07.316196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

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01030446801/08/2020 12:00:00 AM01/08/2020 12:00:00 AM22303Southwest3772624BATTERY - SIMPLE ASSAULT0444 091336FB501.0SINGLE FAMILY DWELLING400.0STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)AOAdult Other624.0NaNNaNNaN1100 W 39TH PLNaN34.0141-118.2978
119010108601/02/2020 12:00:00 AM01/01/2020 12:00:00 AM3301Central1632624BATTERY - SIMPLE ASSAULT0416 1822 141425MH102.0SIDEWALK500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont624.0NaNNaNNaN700 S HILL STNaN34.0459-118.2545
219010108701/02/2020 12:00:00 AM01/01/2020 12:00:00 AM5101Central1562626INTIMATE PARTNER - SIMPLE ASSAULT1414 1218 2000 1814 0416 044753FB502.0MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)400.0STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)ICInvest Cont626.0NaNNaNNaN300 E 5TH STNaN34.0449-118.2458
319150150501/01/2020 12:00:00 AM01/01/2020 12:00:00 AM173015N Hollywood15432745VANDALISM - MISDEAMEANOR ($399 OR UNDER)0329 140276FW502.0MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)NaNNaNICInvest Cont745.0998.0NaNNaN5400 CORTEEN PLNaN34.1685-118.4019
419192126901/01/2020 12:00:00 AM01/01/2020 12:00:00 AM41519Mission19982740VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS)032931XX409.0BEAUTY SUPPLY STORENaNNaNICInvest Cont740.0NaNNaNNaN14400 TITUS STNaN34.2198-118.4468
520010050101/02/2020 12:00:00 AM01/01/2020 12:00:00 AM301Central1631121RAPE, FORCIBLE0413 1822 1262 141525FH735.0NIGHT CLUB (OPEN EVENINGS ONLY)500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont121.0998.0NaNNaN700 S BROADWAYNaN34.0452-118.2534
620010050201/02/2020 12:00:00 AM01/02/2020 12:00:00 AM13151Central1611442SHOPLIFTING - PETTY THEFT ($950 & UNDER)1402 2004 0344 038723MH404.0DEPARTMENT STORENaNNaNICInvest Cont442.0998.0NaNNaN700 S FIGUEROA STNaN34.0483-118.2631
720010050401/04/2020 12:00:00 AM01/04/2020 12:00:00 AM401Central1552946OTHER MISCELLANEOUS CRIME1402 03920XX726.0POLICE FACILITYNaNNaNICInvest Cont946.0998.0NaNNaN200 E 6TH STNaN34.0448-118.2474
820010050701/04/2020 12:00:00 AM01/04/2020 12:00:00 AM2001Central1011341THEFT-GRAND ($950.01 & OVER)EXCPT,GUNS,FOWL,LIVESTK,PROD1822 0344 140223MB502.0MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)NaNNaNICInvest Cont341.0998.0NaNNaN700 BERNARD STNaN34.0677-118.2398
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Last rows

DR_NODate RptdDATE OCCTIME OCCAREAAREA NAMERpt Dist NoPart 1-2Crm CdCrm Cd DescMocodesVict AgeVict SexVict DescentPremis CdPremis DescWeapon Used CdWeapon DescStatusStatus DescCrm Cd 1Crm Cd 2Crm Cd 3Crm Cd 4LOCATIONCross StreetLATLON
8705720050538202/03/2020 12:00:00 AM02/03/2020 12:00:00 AM11205Harbor5141310BURGLARY2038 1813 0329 0344 2000 0913 0352 160641FH502.0MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)NaNNaNAOAdult Other310.0NaNNaNNaN900 N WILMINGTON BLNaN33.7897-118.2787
8705820071049905/30/2020 12:00:00 AM05/30/2020 12:00:00 AM16157Wilshire7231231ASSAULT WITH DEADLY WEAPON ON POLICE OFFICER1212 04470FX101.0STREET500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont231.0NaNNaNNaN3RD STFAIRFAX AV0.00000.0000
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8706020051001205/27/2020 12:00:00 AM05/17/2020 12:00:00 AM6505Harbor5631331THEFT FROM MOTOR VEHICLE - GRAND ($400 AND OVER)0385 1300 1309 034435MW108.0PARKING LOTNaNNaNICInvest Cont331.0NaNNaNNaN10THGRAND33.7352-118.2901
8706120090975405/16/2020 12:00:00 AM05/16/2020 12:00:00 AM509Van Nuys9292626INTIMATE PARTNER - SIMPLE ASSAULT0416 0444 1305 200038FH101.0STREET400.0STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)AOAdult Other626.0NaNNaNNaNFULTON AVVICTORY BL34.1867-118.4225
8706220111029606/11/2020 12:00:00 AM06/05/2020 12:00:00 AM111011Northeast11332354THEFT OF IDENTITY0930 1822 2021 092976MW501.0SINGLE FAMILY DWELLINGNaNNaNICInvest Cont354.0NaNNaNNaN3300 HOLLYDALE DRNaN34.1119-118.2591
8706320111041006/14/2020 12:00:00 AM06/13/2020 12:00:00 AM160011Northeast11892624BATTERY - SIMPLE ASSAULT0444 0913 0602 1202 1701 036077FH501.0SINGLE FAMILY DWELLING400.0STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)ICInvest Cont624.0NaNNaNNaN3400 MARMION WYNaN34.0863-118.2157
8706420040966206/10/2020 12:00:00 AM06/10/2020 12:00:00 AM15004Hollenbeck4731440THEFT PLAIN - PETTY ($950 & UNDER)034457MH203.0OTHER BUSINESSNaNNaNICInvest Cont440.0NaNNaNNaN400 S SOTO STNaN0.00000.0000
8706520181252806/13/2020 12:00:00 AM06/13/2020 12:00:00 AM170018Southeast18291236INTIMATE PARTNER - AGGRAVATED ASSAULT0913 2000 1300 0411 1813 1814 1243 127622FH501.0SINGLE FAMILY DWELLING200.0KNIFE WITH BLADE 6INCHES OR LESSICInvest Cont236.0NaNNaNNaN2200 E 102ND STNaN33.9439-118.2330
8706620170996506/12/2020 12:00:00 AM06/11/2020 12:00:00 AM213017Devonshire17811320BURGLARY, ATTEMPTED16070XX203.0OTHER BUSINESS500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont320.0NaNNaNNaN20900 OSBORNE STNaN34.2330-118.5885